An iterative learning controller with initial state learning

نویسندگان

  • Yang-Quan Chen
  • Changyun Wen
  • Zhiming Gong
  • Mingxuan Sun
چکیده

In iterative learning control (ILC), a common assumption is that the initial states in each repetitive operation should be inside a given ball centered at the desired initial states which may be unknown. This assumption is critical to the stability analysis, and the size of the ball will directly affect the final output trajectory tracking errors. In this paper, this assumption is removed by using an initial state learning scheme together with the traditional D-type ILC updating law. Both linear and nonlinear time-varying uncertain systems are investigated. Uniform bounds for the final tracking errors are obtained and these bounds are only dependent on the system uncertainties and disturbances, yet independent of the initial errors. Furthermore, the desired initial states can be identified through learning iterations.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Iterative learning identification and control for dynamic systems described by NARMAX model

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

متن کامل

Perfect Tracking of Supercavitating Non-minimum Phase Vehicles Using a New Robust and Adaptive Parameter-optimal Iterative Learning Control

In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is of the aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control...

متن کامل

Bilateral Teleoperation Systems Using Backtracking Search optimization Algorithm Based Iterative Learning Control

This paper deals with the application of Iterative Learning Control (ILC) to further improve the performance of teleoperation systems based on Smith predictor. The goal is to achieve robust stability and optimal transparency for these systems. The proposed control structure make the slave manipulator follow the master in spite of uncertainties in time delay in communication channel and model pa...

متن کامل

Repetitive Tracking Control of Nonlinear Systems Using Reinforcement Fuzzy-Neural Adaptive Iterative Learning Controller

This paper proposes a new fuzzy neural network based reinforcement adaptive iterative learning controller for a class of nonlinear systems. Different from some existing reinforcement learning schemes, the reinforcement adaptive iterative learning controller has the advantages of rigorous proofs without using an approximation of the plant Jacobian. The critic is appended into the reinforcement a...

متن کامل

A Nonlinear Iterative Learning Controller for a Finite-Time Convergence against Initial State Error

In this paper, a nonlinear iterative learning control(ILC) algorithm is proposed for a LTI system and it is shown that the e ect of the initial state error can be reached to zero in a given nite time. It is also shown that the bound of error reduction can be e ectively contrlled by tuning gains of the proposed controller. In order to con rm validity of the proposed ILC algorithm, an example is ...

متن کامل

Advanced Iterative Learning Controllers for Robotic Systems

In this paper it is proposed an extended memory iterative learning technique. The knowledge of the iterative learning controller can be built by using the previous tasks of the iterative learning controller in tracking various desired trajectories in terms of a database of input and output data. For a new desired trajectory, iterative learning controller can predict the initial control input fr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • IEEE Trans. Automat. Contr.

دوره 44  شماره 

صفحات  -

تاریخ انتشار 1999